Welcome to the bang-bang world of AI, where the technology speeds ahead, line-of-business (LOB) tech buyers ready their wallets, and channel partners move quickly to keep up.

There’s no question the enterprise market for AI is about to accelerate. Worldwide spending on cognitive and AI systems will reach $19.1 billion in 2018, an increase of more than 54 percent from 2017, according to IDC. By 2019, 40 percent of digital transformation initiatives will use AI.

“Interest and awareness of AI is at a fever pitch,” said David Schubmehl, research director at IDC. “Every industry and every organization should be evaluating AI to see how it will affect their business processes and go-to-market efficiencies.”

Signs point to an emerging channel of systems integrators reinventing themselves as AI experts. In order to cater to them, DataRobot recently launched a partner program that includes a DataRobot University, APIs and SDKs, and an array of support benefits. DataRobot has approximately 80 channel partners.

“We’re going to more than triple our channel revenue this year,” says Seann Gardiner, executive vice president of business development at DataRobot. “I wouldn’t say the majority of our sales today is through the channel, but in places like Japan, we’re 100 percent channel. As we expand to places like Singapore, Australia and Western Europe, certainly the channel is going to be a big part of that. It’ll be a healthy mix.”

Seann Gardiner

Many channel partners, though, still struggle to become AI experts. The problem lies with the new tech buyer — the LOB. Traditional channel partners make technology-laden sales pitches to the CIO, but the LOB wants to know about business outcomes with AI.

Help may be on the way.

DataRobot’s tools are designed for the LOB (although many data scientists use them as well). In fact, a smart business analyst can use them. The DataRobot platform has hundreds of prebuilt models that do the heavy lifting, essentially automating machine learning. The sales pitch to the LOB can be about the business outcome, not the data science of machine learning.

Teknion consultants don’t need to be data scientists, either.

“What has taken companies days and weeks to prove out and build a model and get it to a high quality is done now with the push of a button,” Collins says. “For us, it’s more about how to leverage the DataRobot models, how to prep the data, and making sure we understand the business need.”

William Collins

DataRobot also helps channel partners and LOBs learn about machine learning through its DataRobot University. There are a half-dozen courses. Each course is one to two days of in-person training and costs roughly $500 per day. There’s a course for LOBs to identify applications for machine learning, a course for business analysts to improve machine-learning results, even a course for data scientists to augment what they’re doing with automated machine learning. More than 6,000 people have already taken a course.

“In the next 12-18 months, we’re going to have a certification program for our partners,” Gardiner says. “It’ll be a deeper level of technical training.”

A few of Teknion’s employees have taken a DataRobot University course. Collins says employees are excited to learn more about AI and machine learning. Recruiting young talent is also easier. These are the kinds of internal benefits enjoyed by a channel partner in one of the hottest tech spaces.

Data analytics and machine learning are closely related, the difference being that machine learning trains on data, adapts and gets better at achieving its goals. For health-care customers, machine learning can provide insights into staffing needs, readmit rates, drug interactions. For defense industry customers, machine learning can take predictive maintenance to a new level.

While it’s still early in the partnership – Teknion hasn’t sold DataRobot yet – the upside is boundless. DataRobot’s tools open up machine learning to mid-tier companies that can’t afford a cadre of data scientists. This also frees up companies to bring machine learning to multiple departments.

“Even within our existing customers, we might start with a single department and prove out the use case while knowing we can ultimately roll it out to the whole organization,” Collins says.

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